Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

El-Toukhy, Ahmed T.; Badr, Mahmoud M.; Mahmoud, Mohamed; Srivastava, Gautam; Fouda, Mostafa M.; Alsabaan, Maazen

doi:10.1109/access.2023.3284681

Cited by 17 publications

(4 citation statements)

References 53 publications

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“…Also, information and communication technologies (ICTs) and correlated cyber-threats necessitate proactive measures. There are various studies on energy theft detection handling the consumption data to achieve a high detection rate (DR) and accurate results [ 13 , 14 , 15 , 16 , 17 ]. Many methods are used for energy theft detection, such as statistics, data mining, machine learning (ML), and DL techniques [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Gunduz,

Das

2024

Sensors

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In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

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Section: Introductionmentioning

confidence: 99%

“…Unpredictable attack vectors may be considered zero-day attacks [ 15 ]. We address the challenges posed by zero-day attacks and imbalanced data by generating synthetic attack datasets, leveraging the predictable nature of theft patterns.…”

Section: Introductionmentioning

confidence: 99%

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Gunduz,

Das

2024

Sensors

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“…However, in smart grids, some malicious consumers can manipulate their SMs to report lower metering readings to the electric utility company (EUC) with the aim to decrease their consumption bills [8][9][10][11][12][13][14][15]. Unfortunately, such electricity theft causes high financial losses and overloads the power grids, thus negatively impacting power grid stability all over the world [16].…”

Section: Introductionmentioning

confidence: 99%

Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks

Alshehri,

Badr,

Baza

et al. 2024

Sensors

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Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers’ privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors’ parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers’ privacy.

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“…Smart meters provide an abundance of energy consumption data, encouraging researchers to introduce machine learning (ML) models for the detection of electricity theft [6,7]. These ML-based detectors encompass both supervised classifiers and anomaly detectors, aiming to accurately identify instances of suspicious electricity usage patterns [8].…”

Section: Introductionmentioning

confidence: 99%

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

Habbak,

Mahmoud,

Fouda

et al. 2023

Energies

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In the smart grid, malicious consumers can hack their smart meters to report false power consumption readings to steal electricity. Developing a machine-learning based detector for identifying these readings is a challenge due to the unavailability of malicious datasets. Most of the existing works in the literature assume attacks to compute malicious data. These detectors are trained to identify these attacks, but they cannot identify new attacks, which creates a vulnerability. Very few papers in the literature tried to address this problem by investigating anomaly detectors trained solely on benign data, but they suffer from these limitations: (1) low detection accuracy and high false alarm; (2) the need for knowledge on the malicious data to compute good detection thresholds; and (3) they cannot capture the temporal correlations of the readings and do not address the class overlapping issue caused by some deceptive attacks. To address these limitations, this paper presents a deep support vector data description (DSVDD) based unsupervised detector for false data in smart grid. Time-series readings are transformed into images, and the detector is exclusively trained on benign images. Our experimental results demonstrate the superior performance of our detectors compared to existing approaches in the literature. Specifically, our proposed DSVDD-based schemes have exhibited improvements of 0.5% to 3% in terms of recall and 3% to 9% in terms of the Area Under the Curve (AUC) when compared to existing state-of-the-art detectors.

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Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

Cited by 17 publications

References 53 publications

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks

Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids

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